The need for smaller, higher performance, and more complex electronic devices increases dramatically with each new generation of device. Accordingly the complexity of the process of designing and fabricating semiconductor materials necessary to meet these needs has also increased. With this increase in complexity has come an increase in cost. This increase in cost is due in large part to the critical need for optimal material selection and processing in the semiconductor design and fabrication process. As the complexity demanded by the market increases, the device designs required to provide this desired complexity become increasingly critical.
Traditionally, design engineers have been forced to rely on trial-and-error fabrication runs to gain experience concerning how to create a process and select process parameters that will yield their desired design. As the complexity of the devices demanded continues to increase, this approach of using many time and resource consuming design-fabricate-test iterations becomes increasingly cost prohibitive. Design and process engineers must know how the devices that they wish to fabricate will behave once completed. They must know how the devices that they design will be fabricated. They must know how the materials that they select for fabrication will behave under the changing parameters of the fabrication process. Without this information, the design engineers have no way of knowing an actual laboratory fabrication process can be developed that will yield the device as they have envisioned. The key to solving both of these problems is to provide a system that accurately models both the resulting device and the fabrication process steps necessary to arrive at that device in the laboratory, while allowing for analysis of the device during fabrication and after completion. Without such a dual purpose system, the need remains unfilled.
The existing art shows that some methods of modeling the desired design have been developed. However, these methods focus on the geometry of the desired device and not on the process necessary to arrive at that device. None of these methods accurately models the series of fabrication process steps that the device must be exposed to during an actual fabrication process to achieve the desired design. They focus instead only on the finished design, leaving the process engineer once again to resort to a trial and error process to develop a fabrication process. Because existing methods do not use actual fabrication process steps to arrive at the desired design, they are often more complicated to operate and less intuitive to the user. Because the actual fabrication process steps are not accurately modeled by existing methods, these methods are also incapable of providing for accurate representation of data concerning the properties that any of the materials in the design exhibit either during or after the completion of the fabrication process steps. Due to this lack of a truly realistic process modeling capability, existing methods are incapable for example of determining if the resultant device is actually capable of being fabricated in the laboratory, or if one of the processes in the fabrication will cause failure prior to the completion of the fabrication process.
Due to these shortcomings, existing methods also fail to provide for anything more than a limited analysis of the resulting device. Since the existing methods do not provide for an accurate modeling of the actual fabrication process steps, any and all analyses of the device may not be performed until it has been completely modeled. Thus valuable information that may be gained by analyzing the device at a point during the fabrication process is not available. Furthermore, the traditional mesh-based analysis methods that are available for use in analyzing the completed device are inefficient, time consuming, and woefully inaccurate.